Identifying and Changing Personal Information

ABSTRACT

Systems, apparatuses, and methods for analyzing information about a user are presented which include obtaining at least one search result based on at least one search terms describing the user; presenting the at least one search result to the user; receiving an indication from the user of the desirability of a search result; and performing an action based on the desirability of the search result. Systems, apparatuses, and methods are also presented for determining a reputation score representing the reputation of a user which include collecting search results from data source, determining an effect on the reputation of the user of the search results from the data source, and calculating a reputation score for the user based on the determined effect on the reputation of the user from the search results from the data source.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/038,123, filed Mar. 1, 2011, titled “Identifying and CorrectingPersonal Information,” which is a divisional of U.S. application Ser.No. 12/021,978, filed Jan. 29, 2008, (now U.S. Pat. No. 8,027,975),titled “Identifying and Correcting Personal Information,” which claimsthe benefit of priority to U.S. Provisional Application No. 60/898,899,filed Jan. 31, 2007, titled “Identifying and Correcting PersonalInformation,” all of which are incorporated by reference in theirentirety.

FIELD OF DISCLOSURE

The present inventions relate to methods, systems, and apparatuses forfacilitating the identification of personal information, the changingand/or removal of such information, and the generation of subjectivepersonal reputation scoring or rating based on the informationidentified.

BACKGROUND

Since the early 1990s, the number of people using the World Wide Web andthe Internet has grown at a substantial rate. As more users takeadvantage of the services available on the Internet by registering onwebsites, posting comments and information electronically, or simplyinteracting with companies that post information about others (such asonline newspapers or social networking websites), more and moreinformation about users is publicly available online. Naturally,individuals, organizations, and companies such as professionals,parents, college applicants, job applicants, employers, charities andcorporations have raised serious and legitimate concerns about copingwith the ever-increasing amount of information about them available onthe Internet, because online content about even the most casual Internetusers can be harmful, hurtful, or even false.

The process of evaluating a user in a variety of professional and/orpersonal contexts has become increasingly sensitive to the type andquantity of information available about that user on the Internet. Auser may desire an easy way to assess whether she, or somebody she isinteracting with, has accrued a reputation that is generally positive ornegative or positive or negative with regard to a certain aspect oftheir reputation. Exemplary interactions of a user with another include,for example, beginning a romantic relationship, offering an employmentor business opportunity, or engaging in a financial transaction. As theamount of information about a user available online increases, theprocess of sifting through all of that information, assessing itsrelative import, classifying it, and synthesizing it down to a generalassessment of the user's public, online, reputation becomes moredaunting.

Therefore, there is a need for methods, apparatuses, and systems thatwill allow parties to continue using the Internet while ensuring thatthe information about them on the Internet is not incorrect, slanderous,scandalous, or otherwise harmful to their reputations or well-being.There is also a need for systems that will allow parties to understandrapidly and broadly how their reputations may be perceived by otherindividuals, groups, organizations, and/or companies, based on theinformation available about them on the Internet.

SUMMARY

Presented are systems, apparatuses, and methods for analyzinginformation about a user are presented which include obtaining at leastone search result from a data source based on at least one search termdescribing the user, receiving an indication of the desirability of theat least one search result, and performing an action based on thedesirability of the at least one search result.

Systems, apparatuses, and methods are also presented for determining areputation score representing the reputation of a user which includecollecting at least one search result from a data source determining aneffect on the reputation of the user of the at least one search resultfrom the data source and calculating a reputation score for the userbased on the determined effect on the reputation of the user from the atleast one search result from the data source.

Also presented are systems, apparatuses, and methods for analyzinginformation about a user that include obtaining at least one searchresult based on at least one search term describing the user,determining relevancy of the at least one search result presenting theat least one search result to the user, receiving an indication from theuser of the relevance or desirability of a search result of the at leastone search result, and performing an action based on the desirability ofthe search result.

In some embodiments, the systems, apparatuses, and methods may alsoinclude determining an additional search term based on the at least onesearch result, and using the additional search term to obtain a searchresult. Determining an additional search term may be performedautomatically and/or may be performed by a human agent or the user.

In some embodiments, an indication may be received that a search resultmay be an undesirable search result. The action performed may be causingthe removal or change of the undesirable search result at a data sourcefrom which the undesirable search result was obtained. The undesirablesearch result may contain data about the user that may be incorrect ormay be damaging to the reputation of the user. The action performed mayinclude determining whether the undesirable search result can be changedor removed at a data source from which the undesirable search result wasobtained and, if the undesirable search result can be changed or removedat the data source, causing the change, correction, or removal of theundesirable search result at the data source.

In some embodiments, determining relevancy of the at least one searchresult may include determining whether the at least one search resultcontains information associated with the user and/or ignoring a searchresult if the search result does not contain information associated withthe user. In some embodiments, if the at least one search result doesnot contain information associated with the user, then an exclusionarysearch term may be added in a subsequent search, wherein theexclusionary search term may be designed to exclude the at least onesearch result does not contain information associated with the user.

In some embodiments, obtaining at least one search result may beperformed multiple times and additional steps may be performed, such asgenerating a search ranking system based on the at least one searchresult from the multiple performances of the obtaining step and sortinga further search result based on the search ranking system. Generating asearch ranking system may be performed using a Bayesian network. TheBayesian network may utilize a corpus of irrelevance-indicating tokensand a corpus of relevance-indicating tokens.

In some embodiments, the at least one search result may be obtainedperiodically. Periodicity of performing the obtaining step may bedetermined based on user characteristics or data source characteristics.

In some embodiments, obtaining at least one search result may beperformed multiple times and additional steps may be performed, such asdetermining a signature for a currently-obtained search result,comparing the signature to a previously-obtained signature for apreviously-obtained search result, and determining the relevancy for thesearch result when the currently-obtained signature and thepreviously-obtained signature are different.

In some embodiments, determining relevancy may include presenting the atleast one search result to a human agent, obtaining an indication of acategorization of the at least one search result from the human agent,and automatically categorizing the at least one search result based onthe indication from the human agent.

In some embodiments, obtaining the at least one search result mayinclude receiving at least one search result from, for example, a humanagent or user and determining its relevancy. Determining the relevancyof the search result may include obtaining an indication of acategorization of the at least one search result from, for example, thehuman agent or user, and automatically categorizing the at least onesearch result based on the indication from, for example, the human agentor user.

Systems, methods, and apparatuses are also presented that determine thereputation of a user by collecting data from a data source, determiningthe effect on reputation of the user of the data from the data source,and determining a reputation score for the user based on the effect onreputation of the data from the data source. In some embodiments, thesystems, methods, and apparatuses may further include presenting thereputation score to a third party at the user's request in order tovouch that the user is as reputable as the score indicates, presentingthe reputation score to a third party at the third party's request inorder to vouch that the user may be as reputable as the score indicateswherein the data source includes, for example, a credit agency database,a criminal database, an insurance database, a social networkingdatabase, and/or a news database.

In some embodiments, determining the effect on reputation may includecategorizing an element of the at least one search result according toits mood and/or significance, and basing the effect on reputation on themood and/or significance categorization(s). In some embodiments,determining the effect on reputation may include associating an elementof the at least one search result along a positive to negative scale,and basing the effect on reputation on the positive to negativeassociations.

In some embodiments, determining a reputation score for a user maycomprise determining at least one reputation sub-score for the userbased on the effect on reputation of the search result from the datasource. Types of reputation sub-score may include any appropriatereputational attribute, for example, a reputation as an employee,employer, significant other, lawyer, or reputation as a potentialparent.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of theinvention. In the drawings:

FIG. 1 is a block diagram depicting an exemplary system for analyzinginformation about a user.

FIG. 2 is a flowchart depicting a process changing and/or removing adamaging search result from data sources.

FIG. 3 is a flowchart depicting a process for sorting search results.

FIG. 4 is a flowchart depicting of a process for determining if thesignature recorded for a search result is the same as a previouslyrecorded signature for a search result.

FIG. 5 is a flowchart depicting a process for indicating thecategorization of the search results.

FIG. 6 is a flowchart depicting a process for calculating a reputationscore for a user.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 is a block diagram depicting an exemplary system 100 foranalyzing information about a user. In example system 100, search module120 is coupled to user information processing module 110, data storagemodule 130, and network 140. Search module 120 is also coupled to atleast one data source, such as data sources 151, 152, and/or 153, eithervia network 140 or via other coupling (not pictured). Data sources 151,152, and/or 153, may be proprietary databases containing informationabout one or more users 161, 162, and/or 163. Exemplary data sources151, 152, and/or 153 may be, for example, “blogs,” or websites, such associal networking websites, news agency websites, private partywebsites, or company websites. Exemplary data sources 151, 152, and/or153 may also be cached information stored in a search database, such asthose maintained by Google™ or Yahoo!™. Exemplary data sources 151, 152,and 153 may further be, for example, criminal databases or listings,credit agency data sources, insurance databases, or any electronic orother source of information about user 161, 162, and/or 163. System 100may include any number of data sources 151, 152, and/or 153 and may beused by any number of users, human agents and/or third parties.

One or more users 161, 162, and/or 163 may interact with userinformation processing module 110 through, for example, personalcomputers, personal data devices, telephones, or other devices coupledto the user information processing module 110 via network 140 (notpictured), or via other coupling through which they may interact withinformation processing module 110.

One or more users 161, 162, and/or 163 may directly or indirectlyprovide user information processing module 110 with information orsearch terms that identify a user. User information processing module110 or search module 120 may use the identifying information or searchterms to construct searches to find information, or search results,about a user. The search module 120 then may search a data source 151,152, and/or 153, using at least one search term, for information about auser. A search result about a user may be stored in data storage module130 and/or analyzed by user information processing module 110. Specificembodiments of analyzing and storing data about a user are describedwith respect to FIGS. 2, 3, 4, 5, and 6.

Network 140 may be, for example, the Internet, an intranet, a local areanetwork, a wide area network, a campus area network, a metropolitan areanetwork, an extranet, a private extranet, any set of two or more coupledelectronic devices, or a combination of any of these or otherappropriate networks.

The coupling between modules, or between modules and network 140, mayinclude, but is not limited to, electronic connections, coaxial cables,copper wire, and fiber optics, including the wires that comprise network140. The coupling may also take the form of acoustic or light waves,such as lasers and those generated during radio-wave and infra-red datacommunications. Coupling may also be accomplished by communicatingcontrol information or data through one or more networks to other datadevices.

Each of the logical or functional modules described above may comprisemultiple modules. The modules may be implemented individually or theirfunctions may be combined with the functions of other modules. Further,each of the modules may be implemented on individual components, or themodules may be implemented as a combination of components. For example,user information processing module 110, search module 120, and datastorage module 130 may each be implemented by a field-programmable gatearray (FPGA), an application-specific integrated circuit (ASIC), acomplex programmable logic device (CPLD), a printed circuit board (PCB),a combination of programmable logic components and programmableinterconnects, single CPU chip, a CPU chip combined on a motherboard, ageneral purpose computer, or any other combination of devices or modulescapable of performing the tasks of modules 110, 120, and/or 130. Datastorage module 130 may comprise a random access memory (RAM), a readonly memory (ROM), a programmable read-only memory (PROM), a fieldprogrammable read-only memory (FPROM), or other dynamic storage devicefor storing information and instructions to be used by user infoprocessing module 110 or search module 120. Data storage module 130 mayalso include a database, one or more computer files in a directorystructure, or any other appropriate data storage mechanism such as amemory.

FIG. 2 is a flowchart depicting a process for finding and changingand/or removing a damaging search result about at least one user from adata source. In step 210, instructions for conducting a search includingat least one search term are received by, for example, a system orapparatus. The instructions may be received from, for example, a userdirectly, a third party, or an online data searching service that a useror third party may sign up for. The instructions may also be receivedfrom storage.

A user or third party may sign up for an online data searching servicevia, for example, a personal computer, a personal data device, or via awebsite. When signing up, a user or third party may provide identifyinginformation about themselves or another which may be used by, forexample, an information processing module, or a search module toconstruct searches related to the user or another. In some embodiments,the received instruction and/or at least one search term may be relatedto, for example, a user, a group, an organization, or a company.

In step 220, a search module may obtain at least one search result basedon the received instructions and/or at least one search term. The searchresult may be obtained from a data source. A search result may beobtained via “screen scraping” on a publicly available search engine,such as Google™ search or Yahoo!™ search, or private search engines,such as Westlaw™ search or LexisNexis™ search. A search result may alsobe obtained via a searching application program interface (API) or astructured data exchange (such as eXtensible Markup Language). A searchmay be performed using at least one search term that is provided or isgenerated based on information provided by, for example, a user or athird party. In an exemplary search, a user may provide search termssuch as her home town, city of residence, and alma mater which may beused alone or in conjunction with each other as search terms for asearch. A search result may be obtained automatically or manually by auser, a third party, or a human agent based on the instructions and/orat least one search term. A search result obtained in step 220 may besaved (step 230).

Once a search result is obtained, the relevancy of the search result maybe determined, as in step 240. The relevancy may be determined, forexample, automatically based on the number of times that certain typesof data or elements of a search result are present in the search result.The relevancy of a search result may be based on, for example, the datasource from which it was obtained, the content of the search result, orthe type of search result found. Additionally or alternatively, therelevancy of a search result may be determined directly by, for example,a human agent or a user.

The relevancy of an obtained search result may include determining themood and/or significance of the search result. The mood of a searchresult may include data regarding the content of the search result andmay relate to, for example, the emotional context of the search resultor its data source or the nature of statements within the search result.The determination and/or assignment of a mood to a search result may bebased upon its positive or negative effect on a reputation. Differentportions of a search result may have different moods based on, forexample, their content. Moods and sub-moods may be assigned a numericalvalue. Calculating the impact of a search result's mood or sub-mood on areputation is discussed in further detail below.

Additionally or alternatively, the relevancy of an obtained searchresult may include determining and/or assigning a significance to asearch result or a data source. A significance may range, for example,from high to low. The significance of a search result or a data sourcemay be assigned a weighted value such that more a important searchresult or data source is determined to have and/or assigned a greatersignificance when compared with less a important search result or datasource. A search result or data source may be determined and/or assignedfor example a high, medium, or low significance. The significance of adata source may be determined or assigned based on, for example, thenumber of inbound links to the data source, the number of search enginesthat report inbound links to the data source, or a synthetic measurethat is proportional to the number of inbound links to the data source.Exemplary high significance data sources include MyFace.com™, iTunes™,or NYtimes.com™.

The significance of a search result may be determined and/or assignedbased on, for example, the ratio of references to the search user's nameto the total number of words in a search result, the existence of theuser's name in the title of the search result, font design or graphicelements surrounding the user's name, or the rank of a user's name in aname query of a data source. A data source may be assigned asignificance based on, for example, how frequently it is visited, orwell known data source. Exemplary high significance search results mayinclude the name of a user prominently or repeatedly mentioned on a datasource. Calculating the impact of a search result's mood or sub-mood ona reputation is discussed in further detail below.

In some embodiments, step 240 may include generating a search resultranking system, and/or sorting search results based on the search resultranking system, examples of which are depicted in FIG. 3. In step 250,the search result may be output or displayed to, for example, a user, ahuman agent, or a software program. The relevancy of the search resultmay also be output or displayed to, for example, a user, human agent, orsoftware program. The search result and/or its relevancy may be outputor displayed via email, fax, webpage, or in any other appropriatemanner. The search result and/or its relevancy may be displayed as, forexample, a copy of the original search result, a link to the searchresult, a screen shot of the search result, or any other appropriaterepresentation.

In step 260, an additional search term may be desired for a search. Ifan additional search term is desired for a search, then the additionalsearch term may be used to obtain an additional search result (step270). For example, if a search on a user's name elucidates the city inwhich the user works, then the city name may be added to the searchterms for at least one future search. As an additional example, if a newnickname or username for a user is discovered, then it may be used as anadditional search term for a search. Further, a determination may bemade as to whether a search result is related to the same user. If thesearch result is related to the same user, then a search term may beadded as described above. If a search result is related to a differentuser or otherwise not related to the user, then an exclusionary searchterm may be added to the search terms for a search. For example, if auser is named George Washington, then it may be appropriate to addexclusionary terms as part of step 270 to ensure that search results arenot returned related to “George Washington University,” “PresidentGeorge Washington,” or “George Washington Carver.”

An additional search term for a search may be determined by anyappropriate method. For example, a search result may be presented to auser and the user may select an additional search term. Alternatively, ahuman agent may review a search result and provide additional searchterms. Additional search terms may also be determined automatically by,for example, a search module, a user information processing module, or ahuman agent. The automatic determination of an additional search termmay be based on any appropriate calculation or analysis. For example, ifa particular search term occurred often in prior searches relevant to auser, then the particular search term may be used as an additionalsearch term for a new search.

In step 280, a damaging search result may be flagged. The flagging of asearch result may be implemented electronically by, for example, a user,a human agent, or a computer software program via, for example, a webinterface, an email, mail, or fax to a human agent. A search result maybe flagged by placing an appropriate flag in, for example, a datastorage module or otherwise indicating that the search result is to beremoved or changed.

In step 290, a flagged search result may be removed and/or changed, asappropriate. A user may request that all information about her, in thesearch result, be flagged and changed and/or removed or that onlyspecific information within the search result changed or removed. Theremoval or change of a flagged result may be accomplished via an API fora relevant data source. For example, a structured data source may havean API that allows changing or removing data from the data source. Asearch module, or other appropriate module may use a data source's APIto indicate to the data source that information for a user is to beremoved or changed. Flagged results may also be removed or changed whena user and/or human agent calls, emails, mails, or otherwise contactshuman agents responsible for changing or removing information from thedata source. In some cases, step 290 may include a human agent, such asa lawyer, drafting a letter on behalf of a user to persuade human agentsresponsible for the data source to change or remove data related to theuser. In other cases, step 290 may include initiating civil or criminallawsuits against a human agent or company responsible for a data sourceso that the judiciary may force a human agent or company responsible forthe data source to change or remove the data related to a user.

In some embodiments, steps 220-270 may be performed at regular,irregular, or random intervals. For example, steps 220-270 may beperformed hourly, daily, or at any appropriate interval. Steps 220-270may be performed more often for some users than others based on usercharacteristics such as the likelihood of updates, time zone ofresidence, user preference, etc. Further, steps 220-270 may be performedmore often for some data sources than others. For example, if it isknown that a social networking site is updated more often than a companywebsite, steps 220-270 may be performed more often for the socialnetworking site than the company website.

FIG. 3 is a flowchart depicting a process for sorting search results. Instep 310, the relevancy of an obtained search result is determinedand/or indicated—either automatically or through human intervention asdiscussed above. In step 320, a search result ranking system may begenerated. The search result ranking system may rank search resultsbased on one or more considerations, such as their relevancy, mood, orsignificance, the age of the results, how damaging, beneficial, orharmless the results are to a user, or any other appropriate rankingmeans. In step 330, the search results may be sorted based on theirranking in the search result ranking system. The order in which thesearch results are sorted may define how search results are displayed.For example, search results may be sorted such that the newest and/ormost damaging search result is displayed first, followed by the nextnewest, and/or most damaging search result.

In some embodiments, steps 320 and 330 may be performed using a neuralnetwork, a Bayesian classifier, or any other appropriate means forgenerating a search ranking system. If a Bayesian classifier is used, itmay be built using, for example, human agent and/or user input. In someembodiments, the human agent and/or a user may indicate a search resultas either “relevant” or “irrelevant.” Each time a search result isflagged as “relevant” or “irrelevant,” tokens from that search resultmay be added into an appropriate corpus of data, for example, a“relevance-indicating result corpus” or an “irrelevance-indicatingresult corpus.” Before collecting data for a search, a Bayesian networkmay be seeded, for example, with terms collected from a user (such ashome town, occupation, gender, etc.), or another source. Afterclassifying a search result as relevance-indicating orirrelevance-indicating, the tokens (e.g. words or phrases) in the searchresult may be added to the corresponding corpus. In some embodiments,only a portion of the search result may be added to the correspondingcorpus. For example, common words or tokens, such as “a”, “the,” and“and” may not be added to the corpus.

As part of maintaining the Bayesian classifier, a hash table of tokensmay be generated based on the number of occurrences of a token in acorpus. Additionally, a “conditionalProb” hash table may also begenerated for a token in either or both of the corpora to indicate theconditional probability that a search result containing that token isrelevance-indicating or irrelevance-indicating. The conditionalprobability that a search result is relevant or irrelevant may bedetermined based on any appropriate calculations which may, in turn, bebased on the number of occurrences of the token in therelevance-indicating and irrelevance-indicating corpora. For example,the conditional probability that a token is irrelevant to a user may bedefined by the equation:

prob=max(MIN_RELEVANTPROB,min(MAX_IRRELEVANT_PROB,irrelevatProb/total)),

where:

-   -   MIN_RELEVANT_PROB=0.01 (a lower threshold on relevance        probability),    -   MAX_IRRELEVANTPROB=0.99 (an upper threshold on relevance        probability),    -   Let r=RELEVANT_BIAS*(the number of time the token appeared in        the “relevance-indicating” corpus),    -   Let i=IRRELEVANT_BIAS*(the number of time the token appeared in        the “irrelevance-indicating” corpus),    -   RELEVANT_BIAS=2.0,    -   IRRELEVANT_BIAS=1.0 (In some embodiments, “relevance-indicating”        terms should be biased more highly than “irrelevance-indicating”        terms in order to bias toward false positives and away from        false negatives, which is why relevant bias may be higher than        irrelevant bias),    -   nrel=total number of entries in the relevance-indicating corpus,    -   nirrel=total number of entries in the irrelevance-indicating        corpus,    -   relevantProb=min(1.0, r/nrel),    -   irrelevantProb=min(1.0, i/nirrel), and    -   total=relevantProb+irrelevantProb.

In some embodiments, if the relevance-indicating andirrelevance-indicating corpora are seeded and a particular token isgiven a default conditional probability of irrelevance, then theconditional probability calculated as above may be averaged with adefault value. For example, if a user specified that he went to collegeat Harvard, the token “Harvard” may be indicated as arelevance-indicating seed and the conditional probability stored for thetoken Harvard may be 0.01 (only a 1% chance of irrelevance). In thatcase, the conditional probability calculated as above may be averagedwith the default value of 0.01.

In some embodiments, if there is less than a certain threshold ofentries for a particular token in either corpora or in the two corporacombined, then conditional probability that the token isirrelevance-indicating may not be calculated. When the relevancy of asearch result is indicated the conditional probabilities that tokens areirrelevance-indicating may be updated based on the newly indicatedsearch results as part, for example, of step 320.

When a new search result is obtained, the contents of the search resultmay be broken down into at least one token. The probability that a tokenis relevance-indicating and/or irrelevance-indicating may then bedetermined based on, for example, a ranking system. The highestprobabilities of relevance-indication and/or irrelevance-indicationamong the token(s) may then be used to calculate a Bayesian probability.For example, if the highest N probabilities were placed in an arraycalled “probs” then the Bayesian combined probability may be calculatedbased on the Naive Bayes Classifier rule as follows:

$\frac{\prod\limits_{i = 1}^{N}\; {{probs}(i)}}{{\prod\limits_{i = 1}^{N}\; {{probs}(i)}} + {\prod\limits_{i = 1}^{N}\; ( {1 - {{probs}(i)}} )}}.$

The search results may be sorted by the probability that each searchresult is relevant and/or irrelevant.

The Bayesian probability calculated above may represent the probabilitythat the search result is “relevant” and/or “irrelevant.” This is justone formulation of the repeated application of the Bayes Theorem. Otherformulations may also be used to calculate a conditional probabilitybased on unconditional probabilities, such as one or more formulationsdescribed at, for example, Papoulis, A. “Bayes' Theorem in Statistics”and “Bayes' Theorem in Statistics (Reexamined),” §3-5 and 4-4 inProbability, Random Variables, and Stochastic Processes, (2nd ed. NewYork: McGraw-Hill, pp. 38-39, 78-81, and 112-114, 1984, hereinafter“(Papoulis 1984)”). Exemplary alternate forms of Bayes' Theorem,described at (Papoulis 1984) at pp. 38-39, may also be used to calculatethe probability that a search result is “relevant” and/or “irrelevant.”A similar process may be used to associate and/or determine the moodand/or significance of a search result or data source.

FIG. 4 is a flowchart depicting a process for determining whether asignature recorded for a current search result is the same as apreviously recorded signature. A signature of a search result may be,for example, a hash of the relevant web page, an abbreviated form of asearch result or information from the search result, a hash of thesearch result or other computation based on the contents of the search.For example, the hash may be based on the complete search result or aportion of the search result, such as a portion of the search resultsurrounding at least one search term. A search result may include, forexample, a website or web page within the website. A signature recordedfor a search result may include information identifying the searchresult, such as a universal resource locator (URL) for the searchresult, or a classification of the type of search result, and/or asignature of a web site. The signature of a search result may then, instep 420, be compared with a previously-obtained signature of apreviously-obtained search result.

In step 430, it may be determined whether the signature of a currentsearch result is the same as the signature of a previously-obtainedsearch result. If the current search result is the same as thepreviously-obtained search result, the current search result may not beanalyzed further and the process depicted in FIG. 4 may end. If thesignatures of the current and previously-obtained search results differ,the contents of the current search result may be analyzed further (step440). For example, if a social networking site contains informationabout a user and the site is searched on a daily basis on behalf of auser, then a signature (such as a hash of the relevant web page) of themost recently-obtained search result may be compared to the signature ofa previously-obtained search result. If the two signatures areidentical, then the contents of the search results have not changed andthere may be no need to further analyze the most recently obtainedsearch results, at least until the source is next searched.

FIG. 5 is a flowchart depicting a process for indicating thecategorization of search results. In step 510, a search result may bepresented to, for example, a user and/or human agent via, for example, aweb interface, a graphical user interface of a computer program, or viaany other appropriate means. The displayed search result may be obtainedvia any appropriate means. For example, when a search is performed viaone or more public search engines (e.g., Google™ or Yahoo!™), privatesearch systems (e.g., LexisNexis™ or Westlaw™), or any data source, theresult of the search may be displayed to, for example, the user and/orhuman agent.

In step 520, a search result may be identified by, for example, a humanagent or user. In step 525, a classification for the search result maybe determined. The classification may be determined by, for example, ahuman agent, a user, or a Bayesian classifier. Exemplary classificationsinclude: relevancy to the user, how damaging the results are to theuser, or the source type of the search results (social networking site,news database, etc). A search result may be classified based on, forexample, the judgment of a human agent or a user, standard rules (e.g.,any page referring to the user that contains an expletive may be flaggedas damaging), or rules specific to the user (e.g., a user may requestthat all references to her and her previous job be flagged as damaging).

In step 530, the categorization of the search result may be indicated toan appropriate system or module. For example, if a human agent is usinga web browser to search for information about a user and determines thata search result should be classified as damaging, then the human agentmight use her computer mouse to “click on” a “bookmarklet” to indicatethat the search result may be damaging. Classification may be indicatedvia, for example, “bookmarklets,” programmable buttons, user interfaceelements, or any other appropriate means. A bookmarklet or programmablebutton may be a computer program running, at least in part, as part of aweb browser or may be a computer program coupled to a web browser. Abookmarklet is a graphical button that, when clicked on, may cause ascript or program to execute which may send a user informationprocessing module, a server module, or any other appropriate module anindication that a search result is to be flagged. A user interfaceelement, when selected, may cause actions to be performed which mayindicate that a viewed search result is to be flagged. The search resultand the flag or flags associated with it may be stored in a data storagemodule. The indicated flag may be used, in part, to determine therelevancy of a search result or may be shown when the search results aredisplayed.

FIG. 6 is a flowchart depicting a process for calculating a reputationscore. A reputation score may represent, for example, a user'sreputation generally, or as an employee, employer, significant other,potential parent, or any other appropriate dimension or consideration.Further, a reputation score may comprise one or more reputationsub-scores that may be based on sub-elements of a user's reputation,such as specific domains of knowledge or types of interactions. Forexample, a reputation score for a person generally may comprisesub-scores for their reputation as an individual, business associate,employee, employer, significant other, lawyer, or potential parent. Areputation score may be based on other scores and information such as acredit score, an eBay seller score, or karma on a website likeSlashdot™, or any other appropriate building block.

The steps in FIG. 6 may be performed to determine a single reputationscore, multiple types of reputation scores, or one or more reputationsub-scores, any of which may be combined to calculate an aggregatedreputation score. Reputational scoring is a means, for example, iteffect on the reputation score of the user reducing search resultsregarding, for example, a user, to a simple summary score, grade, or anyother appropriate measure. Reputation scoring may allow, for example, auser or human agent to focus on major online impact items that mayaffect a reputation score. A reputation score also allows, for example,a user or human agent to track relevant changes to data, a signature ofa search result, and/or a search result.

In step 610, search results are aggregated. The aggregated searchresults may be any data related to, for example, a user or third party,from any data source. The aggregated search results may be data that isobtained via for example, the processes of FIGS. 2, 3, 4, and/or 5.Aggregated search results may also be data collected via other means ormay be submitted directly by, for example, a user or human agent.

In step 620, aggregated search results are analyzed to determine theireffect on a reputation. This determination may be manual or automatic.For example, a human agent or user may flag a search result, or asegment of the search result, from the aggregated search results asdamaging or benefiting certain aspects of a user's reputation. A humanagent or user may then indicate along one or more spectrums how thesearch result affects a reputation score.

Determining the effect of aggregated search results on a reputationscore may be performed by analyzing a search result and indicating basedon, for example, the content, mood or significance of the search result.In some cases, this determination and/or its indication is automatic.For example, if a user's reputation as an employer was being determinedand the aggregated search results include postings discussing the useron a website designated as a place for posting information about “badbosses,” then an indication may be automatically generated to indicatethat the web posting may be damaging to the user's reputation as anemployer.

In some embodiments, a system may determine whether a search resultpositively or negatively affects the reputation of a user by determiningwhether any of the tokens surrounding the relevancy indicating tokensare contextually “positive” or contextually “negative.” The set ofsurrounding tokens may be defined as the set of tokens within N of therelevance-indicating token, where N is any positive integer. In someembodiments, the set of surrounding tokens may be defined as all of thetokens in a search result or may be defined in any other appropriatemanner. The system may determine whether the surrounding tokens arecontextually-positive by looking them up in tables or databases ofcontextually-positive tokens. A parallel procedure may be used toidentify contextually-negative tokens. For example, search results thatreference the user and contain an expletive within N tokens of arelevancy-indicating token may be automatically categorized as damagingto the user's reputation score.

Furthermore, a reputation score may be calculated partially based on anycontextually-positive, contextually-negative and/or mood indicatingtokens found in the set of tokens surrounding a relevancy-indicatingtoken. Contextually-negative or bad mood tokens may adversely affect orotherwise numerically lower the user's reputation score. Whilecontextually-positive or good mood tokens may numerically increase orotherwise improve a reputation score. In some embodiments,contextually-positive and/or contextually-negative tokens may havenumerical weights or multipliers associated with them. Likewise,numerical weights or multipliers may be associated with a token based ontheir relevancy and/or significance. The more heavily weighted tokensmay have a greater effect on the user's reputation score. Some positiveand negative context determinations may also be user-specific. Forexample, a posting on a website discussing a party that mentions a usermay be more damaging to the reputation score for a minister than for acollege student. Step 620 may also include automatic determinationsand/or determinations by one or more users or human agents regarding theeffect of search results on a reputation score.

In step 630, a reputation score may be calculated. A reputation scoremay be based on any appropriate calculation. For example, a reputationscore may be a sum of the number of positive references minus the sum ofthe number of negative references in the aggregated search results. Areputation score may also be a weighted sum or average of the aggregatedsearch results' effect on the reputation of a user. Additionally oralternatively, a reputation score may also be a sum or a weightedaverage of reputation sub-scores, which may be calculated as describedabove.

Once a reputation score has been calculated, it may be reported to therequesting party, as in step 640. For example, if a potential employeewanted to know the reputation of an employer, then the potentialemployee may request a report of the reputation score of the employer.The reputation score may also be reported to a user.

In some embodiments, the reputation score may be reported to a thirdparty at the request of a user and the party calculating and presentingthe reputation score per one of the embodiments herein may be “vouching”for the user when presenting the user's reputation score. For example,if a user were attempting to become a roommate of another person and theuser's reputation score were reported to the other person by areputation reporting company, then the reputation reporting companywould be vouching that the user was as reputable as the user reputationscore indicates.

The steps depicted in the exemplary flowcharts of FIGS. 2, 3, 4, 5, and6 may be performed by user information processing module 110, by searchmodule 120, or by any other appropriate module, device, apparatus, orsystem. Further, some of the steps may be performed by one module,device, apparatus, or system and other steps may be performed by one ormore other modules, devices, apparatuses, or systems. Additionally, insome embodiments, the steps of FIGS. 2, 3, 4, 5, and 6 may be performedin a different order and/or with fewer or more steps than depicted inthe FIGS. or descriptions herein.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A method for analyzing information about a user, the methodcomprising: obtaining at least one search result from a data sourcebased on at least one search term describing the user; receiving anindication of the desirability of the at least one search result; andperforming an action based on the desirability of the at least onesearch result.